Defensive Distillation

Defensive distillation is a machine learning security technique designed to protect neural networks against adversarial attacks, particularly in classification tasks. It involves training a model on softened probability distributions (using a high temperature parameter) from a pre-trained teacher model, which makes the resulting student model more robust to small input perturbations. This method aims to smooth the decision boundaries of the model, reducing its sensitivity to adversarial examples crafted to cause misclassification.

Also known as: Distillation Defense, Adversarial Distillation, Security Distillation, Defensive Model Distillation, Distillation for Robustness

Why learn Defensive Distillation?

Developers should learn and use defensive distillation when building machine learning systems, especially in security-critical applications like autonomous vehicles, fraud detection, or medical diagnosis, where adversarial attacks could have severe consequences. It is particularly relevant for deep neural networks in image or text classification, as it provides a defense mechanism without requiring significant architectural changes, though it should be combined with other techniques for comprehensive security.

Learning Resources

• docs: Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
• tutorial: Adversarial Machine Learning - Defensive Distillation Tutorial
• course: Machine Learning Security - Defensive Techniques Course
• video: Defensive Distillation Explained - YouTube Video